ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics (1908.06295v1)

Abstract: Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient end-to-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. Based on ShellConv we further build an efficient neural network named ShellNet to directly consume the point clouds with larger receptive fields while maintaining less layers. We demonstrate the efficacy of ShellNet by producing state-of-the-art results on object classification, object part segmentation, and semantic scene segmentation while keeping the network very fast to train.

• The paper introduces the ShellConv operator that uses concentric spherical shells to resolve point order ambiguity and enable efficient local feature aggregation.
• The paper presents the ShellNet architecture, which leverages fewer layers with larger receptive fields for faster training and improved accuracy.
• The paper demonstrates state-of-the-art results in object classification, part segmentation, and semantic scene segmentation on 3D point clouds.

ShellNet: A Novel Approach for Efficient 3D Point Cloud Neural Networks

The paper focuses on advancing the field of 3D point cloud data processing using deep learning techniques, a domain that traditionally faced challenges due to the unordered nature of point clouds. The authors propose a novel convolutional neural network framework named ShellNet, which utilizes a unique convolutional operator called ShellConv. This operator leverages concentric spherical shell statistics for efficient point cloud convolution, addressing the core issues of point order ambiguity and computational inefficiency.

Key Contributions

1. ShellConv Operator: The paper introduces ShellConv, an innovative convolutional operator that resolves the inherent point order ambiguity in point clouds. By partitioning the spatial domain into concentric spherical shells, ShellConv systematically aggregates local features using max-pooling, facilitating permutation invariance while maintaining computational efficiency. This approach enables ShellConv to process point sets as convolutional units efficiently.
2. ShellNet Architecture: Using ShellConv as a foundational building block, the paper presents ShellNet, a streamlined neural network architecture tailored for 3D point cloud processing. ShellNet maintains a reduced number of layers while offering larger receptive fields, thus achieving comparable or superior results with less computational complexity and faster training times than contemporary models.
3. State-of-the-Art Performance: The paper demonstrates the effectiveness of ShellNet across several key scene understanding tasks, namely object classification, object part segmentation, and semantic scene segmentation. The results often surpass existing models, emphasizing both the accuracy and training efficiency of the proposed method.

Implications and Future Directions

The introduction of ShellConv and ShellNet represents a significant step forward in efficiently handling 3D data within neural networks, potentially influencing various applications in computer vision, robotics, and augmented reality where 3D data is prevalent. The proposed method notably reduces computational complexity, which is crucial for real-time applications and deployment on resource-constrained devices.

The paper suggests potential areas for future research, such as extending the framework to object detection and semantic instance segmentation or adapting the methodology for learning with mesh data. Furthermore, integrating this approach within autoencoders could open new possibilities for point cloud generation, significantly impacting 3D modeling and synthesis.

Overall, the paper provides a compelling case for ShellConv and ShellNet as effective tools for the ongoing development and practical application of 3D deep learning techniques. The proposed approach could serve as a foundational element for further innovations in efficiently learning from unordered 3D point sets.